amplement weidhting according to L. Bourdev and J. Brandt paper "Robust Object Detection Via Soft Cascade"

apps/sft/include/sft/octave.hpp

@@ -158,7 +158,7 @@ protected:
     float predict( const Mat& _sample, const cv::Range range) const;
 private:
     void traverse(const CvBoostTree* tree, cv::FileStorage& fs, const float* th = 0) const;
-
+    virtual void initial_weights(double (&p)[2]);
     cv::Rect boundingBox;
 
     int npositives;

apps/sft/octave.cpp

@@ -295,6 +295,7 @@ void sft::Octave::generateNegatives(const Dataset& dataset)
     dprintf("Processing negatives finished:\n\trequested %d negatives, viewed %d samples.\n", nnegatives, total);
 }
 
+
 template <typename T> int sgn(T val) {
     return (T(0) < val) - (val < T(0));
 }
@@ -378,6 +379,13 @@ void sft::Octave::write( cv::FileStorage &fso, const Mat& thresholds) const
         << "}";
 }
 
+void sft::Octave::initial_weights(double (&p)[2])
+{
+    double n = data->sample_count;
+    p[0] =  n / (double)(nnegatives) ;
+    p[1] =  n / (double)(npositives);
+}
+
 bool sft::Octave::train(const Dataset& dataset, const FeaturePool& pool, int weaks, int treeDepth)
 {
     CV_Assert(treeDepth == 2);